GANosaic: Mosaic Creation with Generative Texture Manifolds
This paper presents a novel framework for generating texture mosaics with convolutional neural networks. Our method is called GANosaic and performs optimization in the latent noise space of a generative texture model, which allows the transformation of a content image into a mosaic exhibiting the visual properties of the underlying texture manifold. To represent that manifold, we use a state-of-the-art generative adversarial method for texture synthesis, which can learn expressive texture representations from data and produce mosaic images with very high resolution. This fully convolutional model generates smooth (without any visible borders) mosaic images which morph and blend different textures locally. In addition, we develop a new type of differentiable statistical regularization appropriate for optimization over the prior noise space of the PSGAN model.
Code (0)
등록된 구현이 없습니다.
Tasks
MORPHTexture SynthesisSimilar Papers 제목 키워드 기반
Generative Phomosaic with Structure-Aligned and Personalized Diffusion
We present the first generative approach to photomosaic creation. Traditional photomosaic methods rely on a large number of tile images and color-based matching, which limits both diversity and structural consistency. Ou…
Hyperspectral 3D Mapping of Underwater Environments
Hyperspectral imaging has been increasingly used for underwater survey applications over the past years. As many hyperspectral cameras work as push-broom scanners, their use is usually limited to the creation of photo-mo…
3D ReconstructionSimultaneous Localization and MappingUser-Controllable Multi-Texture Synthesis with Generative Adversarial Networks
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be gener…
DescriptiveTexture SynthesisConstruction of extended 3D field of views of the internal bladder wall surface: a proof of concept
3D extended field of views (FOVs) of the internal bladder wall facilitate lesion diagnosis, patient follow-up and treatment traceability. In this paper, we propose a 3D image mosaicing algorithm guided by 2D cystoscopic …
Image RegistrationJoint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization
Image demosaicing and super-resolution are two important tasks in color imaging pipeline. So far they have been mostly independently studied in the open literature of deep learning; little is known about the potential be…
DemosaickingGenerative Adversarial NetworkImage ReconstructionSSIM+1